Open AccessOnline machine learning algorithms to optimize performances of complex wireless communication systems
Author(s) -
Koji Oshima,
AUTHOR_ID,
Daisuke Yamamoto,
Atsuhiro Yumoto,
Song-Ju Kim,
Yusuke Ito,
Mikio Hasegawa,
AUTHOR_ID,
AUTHOR_ID
Publication year - 2021
Publication title -
mathematical biosciences and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.451
H-Index - 45
eISSN - 1551-0018
pISSN - 1547-1063
DOI - 10.3934/mbe.2022097
Subject(s) - computer science , reinforcement learning , wireless , machine learning , artificial intelligence , simple (philosophy) , online machine learning , algorithm , distributed computing , unsupervised learning , telecommunications , philosophy , epistemology
Data-driven and feedback cycle-based approaches are necessary to optimize the performance of modern complex wireless communication systems. Machine learning technologies can provide solutions for these requirements. This study shows a comprehensive framework of optimizing wireless communication systems and proposes two optimal decision schemes that have not been well-investigated in existing research. The first one is supervised learning modeling and optimal decision making by optimization, and the second is a simple and implementable reinforcement learning algorithm. The proposed schemes were verified through real-world experiments and computer simulations, which revealed the necessity and validity of this research.